organizing

@miSim/initialize.m

@@ -138,6 +138,11 @@ function [obj] = initialize(obj, domain, agents, barrierGain, barrierExponent, m
         % Initialize variable that will store barrier function values per timestep for analysis purposes
         obj.barriers = NaN(obj.numBarriers, size(obj.times, 1));
 
+        % Initialize constraint adjacency history (nAgents x nAgents x nTimesteps)
+        nAgents = size(obj.agents, 1);
+        obj.constraintAdjacencyHist = false(nAgents, nAgents, size(obj.times, 1));
+        obj.constraintAdjacencyHist(:, :, 1) = obj.constraintAdjacencyMatrix;
+
         % Set up plots showing initialized state
         obj = obj.plot();
 

@miSim/miSim.m

@@ -27,6 +27,7 @@ classdef miSim
         spatialPlotIndices = [6, 4, 3, 2];
         numBarriers = 0; % Number of barrier functions needed
         barriers = []; % log barrier function values at each timestep for analysis
+        constraintAdjacencyHist = []; % log constraint adjacency matrix at each timestep
     end
 
     properties (Access = private)

@miSim/run.m

@@ -34,6 +34,11 @@ function [obj] = run(obj)
             obj = obj.lesserNeighbor();
         end
 
+        % Log constraint adjacency for this timestep
+        if coder.target('MATLAB')
+            obj.constraintAdjacencyHist(:, :, ii) = obj.constraintAdjacencyMatrix;
+        end
+
         % Moving
         % Iterate over agents to simulate their unconstrained motion
         for jj = 1:size(obj.agents, 1)

@miSim/teardown.m

@@ -20,6 +20,7 @@ function obj = teardown(obj)
     out.dampingCoeff = obj.dampingCoeff;
     out.useDoubleIntegrator = obj.useDoubleIntegrator;
     out.useFixedTopology = obj.useFixedTopology;
+    out.constraintAdjacency = obj.constraintAdjacencyHist(:, :, 1:(end - 1));
     for ii = 1:size(obj.agents, 1)
         out.agent(ii).pos = squeeze(obj.posHist(ii, 1:(end - 1), 1:3));
         out.agent(ii).vel = squeeze(obj.velHist(ii, 1:(end - 1), 1:3));
@@ -43,6 +44,7 @@ function obj = teardown(obj)
     obj.agents = cell(0, 1);
     obj.adjacency = NaN;
     obj.constraintAdjacencyMatrix = NaN;
+    obj.constraintAdjacencyHist = [];
     obj.partitioning = NaN;
     obj.performance = 0;
     obj.barrierGain = NaN;

plot_1.m

@@ -1,189 +0,0 @@
-clear;
-% Load data
-dataPath = fullfile('.', 'sandbox', 'plot1');
-simHists = dir(dataPath); simHists = simHists(3:end);
-simInits = simHists(endsWith({simHists.name}, 'miSimInits.mat'));
-simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
-assert(length(simHists) == length(simInits), "input data availability mismatch");
-
-% Initialize plotting data
-nRuns = length(simHists);
-Cfinal = NaN(nRuns, 1);
-n = NaN(nRuns, 1);
-doubleIntegrator = NaN(nRuns, 1);
-numObjective = NaN(nRuns, 1);
-positions = cell(6, nRuns);
-commsRadius = NaN(nRuns, 1);
-collisionRadius = NaN(nRuns, 1);
-
-% Aggregate relevant data
-for ii = 1:length(simHists)
-    initName = strrep(simInits(ii).name, "_miSimInits.mat", "");
-    histName = strrep(simHists(ii).name, "_miSimHist.mat", "");
-    assert(initName == histName);
-
-    init = load(fullfile(simInits(ii).folder, simInits(ii).name));
-    hist = load(fullfile(simHists(ii).folder, simHists(ii).name));
-
-    % Stash relevant data
-    Cfinal(ii) = hist.out.perf(end) / init.objectiveIntegral;
-    n(ii) = init.numAgents;
-    doubleIntegrator(ii) = init.useDoubleIntegrator;
-    numObjective(ii) = size(init.objectivePos, 1);
-    commsRadius(ii) = unique(init.comRange);
-    collisionRadius(ii) = unique(init.collisionRadius);
-    for jj = 1:length(hist.out.agent)
-        alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
-        positions{jj, ii} = hist.out.agent(jj).pos;
-        assert(hist.out.agent(jj).commsRadius == commsRadius(ii));
-        assert(hist.out.agent(jj).collisionRadius == collisionRadius(ii));
-    end
-
-    alphaDist2 = unique(alphaDist);
-    if length(alphaDist2) > 1
-        alphaDist2 = alphaDist2(1);
-    end
-    if doubleIntegrator(ii) && all(alphaDist(1:n(ii), ii) == alphaDist2) && numObjective(ii) == 1
-        a2betaIdx = ii;
-        a2beta = struct("init", init, "hist", hist.out);
-    end
-end
-
-commsRadius = unique(commsRadius); assert(isscalar(commsRadius));
-collisionRadius = unique(collisionRadius); assert(isscalar(collisionRadius));
-sensors = flip(unique(alphaDist(1, :)));
-n_unique = sort(unique(n));
-nGroups = length(n_unique);
-
-% Build config label for each run
-config = strings(nRuns, 1);
-baseConfig = strings(nRuns, 1);
-for ii = 1:nRuns
-    s = "";
-    if numObjective(ii) == 1
-        s = s + "A";
-    elseif numObjective(ii) == 2
-        s = s + "B";
-    end
-    if alphaDist(1, ii) == sensors(1)
-        s = s + "_I";
-    elseif alphaDist(1, ii) == sensors(2)
-        s = s + "_II";
-    end
-    if ~doubleIntegrator(ii)
-        s = s + "_alpha";
-    else
-        s = s + "_beta";
-    end
-    baseConfig(ii) = s;
-    config(ii) = n(ii) + "_" + s;
-end
-configOrder = unique(baseConfig(n == n_unique(1)), 'stable');
-nConfigsPerN = length(configOrder);
-
-%%
-close all;
-f1 = figure;
-x1 = axes;
-
-C_mean = NaN(nGroups, nConfigsPerN);
-C_var = NaN(nGroups, nConfigsPerN);
-for ii = 1:nGroups
-    for jj = 1:nConfigsPerN
-        mask = (n == n_unique(ii)) & (baseConfig == configOrder(jj));
-        C_mean(ii, jj) = mean(Cfinal(mask));
-        C_var(ii, jj) = var(Cfinal(mask));
-    end
-end
-
-hBar = bar(x1, C_mean);
-hold(x1, 'on');
-for jj = 1:nConfigsPerN
-    xPos = hBar(jj).XEndPoints;
-    errorbar(x1, xPos, C_mean(:, jj), C_var(:, jj), 'k.', 'LineWidth', 1, 'HandleVisibility', 'off');  % disabled the error bars because they are small to the point of meaninglessness
-end
-hold(x1, 'off');
-set(x1, 'XTickLabel', string(n_unique));
-xlabel("Number of agents");
-ylabel("Final coverage (normalized)");
-title("Final performance of parameterizations");
-legend(["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex", "Location", "northwest");
-grid("on");
-ylim([0, 1/2]);
-
-savefig(f1, "plot1.fig");
-exportgraphics(f1, "plot1.png");
-
-%%
-f2 = figure;
-x2 = axes;
-
-% Compute pairwise distances between agents
-maxPairs = nchoosek(6, 2);
-pairDist = cell(maxPairs, nRuns);
-for ii = 1:nRuns
-    pp = 0;
-    for jj = 1:n(ii)-1
-        for kk = jj+1:n(ii)
-            pp = pp + 1;
-            pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
-        end
-    end
-end
-
-% Cap pairwise distances at communications range
-for ii = 1:nRuns
-    nPairs = nchoosek(n(ii), 2);
-    for pp = 1:nPairs
-        pairDist{pp, ii} = min(pairDist{pp, ii}, commsRadius);
-    end
-end
-
-% Compute mean, min, max pairwise distance across all pairs and timesteps per run
-meanPairDist = NaN(nRuns, 1);
-minPairDist = NaN(nRuns, 1);
-maxPairDist = NaN(nRuns, 1);
-for ii = 1:nRuns
-    nPairs = nchoosek(n(ii), 2);
-    D = vertcat(pairDist{1:nPairs, ii});
-    meanPairDist(ii) = mean(D);
-    minPairDist(ii) = min(D);
-    maxPairDist(ii) = max(D);
-end
-
-% Group pairwise distance stats by (n, config), aggregating across reps
-meanD = NaN(nGroups, nConfigsPerN);
-minD = NaN(nGroups, nConfigsPerN);
-maxD = NaN(nGroups, nConfigsPerN);
-for ii = 1:nGroups
-    for jj = 1:nConfigsPerN
-        mask = (n == n_unique(ii)) & (baseConfig == configOrder(jj));
-        meanD(ii, jj) = mean(meanPairDist(mask));
-        minD(ii, jj) = min(minPairDist(mask));
-        maxD(ii, jj) = max(maxPairDist(mask));
-    end
-end
-
-% Plot whiskers (min to max) with mean markers
-barWidth = 0.8;
-groupWidth = barWidth / nConfigsPerN;
-hold(x2, 'on');
-for jj = 1:nConfigsPerN
-    xPos = (1:nGroups) + (jj - (nConfigsPerN + 1) / 2) * groupWidth;
-    errorbar(x2, xPos, meanD(:, jj), meanD(:, jj) - minD(:, jj), maxD(:, jj) - meanD(:, jj), ...
-        'o', 'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 10);
-end
-hold(x2, 'off');
-set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(n_unique));
-xlabel(x2, "Number of agents");
-ylabel(x2, "Pairwise distance");
-title(x2, "Pairwise Agent Distances (min/mean/max)");
-legend(x2, ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"], "Interpreter", "latex");
-grid(x2, "on");
-
-yline(collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
-yline(commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
-ylim([0, commsRadius + 5]);
-
-savefig(f2, "plot2.fig");
-exportgraphics(f2, "plot2.png");

plot_3.m

@@ -1,142 +0,0 @@
-clear;
-% Load data
-dataPath = fullfile('.', 'sandbox', 'plot3');
-simHists = dir(dataPath); simHists = simHists(3:end);
-simInits = simHists(endsWith({simHists.name}, 'miSimInits.mat'));
-simHists = simHists(endsWith({simHists.name}, 'miSimHist.mat'));
-assert(length(simHists) == length(simInits), "input data availability mismatch");
-assert(isscalar(simHists));
-
-init = fullfile(simInits(1).folder, simInits(1).name);
-hist = fullfile(simHists(1).folder, simHists(1).name);
-
-init = load(init);
-hist = load(hist);
-hist = hist.out;
-
-f3 = figure;
-x3 = axes;
-assert(size(init.objectivePos, 1) == 1)
-assert(hist.useDoubleIntegrator);
-
-plot(hist.perf./init.objectiveIntegral, "LineWidth", 2);
-hold("on");
-for ii = 1:length(hist.agent)
-    plot(hist.agent(ii).perf./init.objectiveIntegral, "LineWidth", 2);
-end
-grid("on");
-ylabel("Performance (normalized)");
-xlabel("Timestep");
-legend(["Cumulative"; "Agent 1"; "Agent 2"; "Agent 3"; "Agent 4"], "Location", "northwest");
-title("$AII\beta$ Performance", "Interpreter", "latex");
-
-savefig(f3, "plot3.fig");
-exportgraphics(f3, "plot3.png");
-
-f4 = figure;
-x4 = axes;
-
-% Compute pairwise distances between agents over time
-nAgents = length(hist.agent);
-commsRadius = hist.agent(1).commsRadius;
-collisionRadius = hist.agent(1).collisionRadius;
-nPairs = nchoosek(nAgents, 2);
-T = size(hist.agent(1).pos, 1);
-pairDistMat = NaN(T, nPairs);
-pp = 0;
-for jj = 1:nAgents-1
-    for kk = jj+1:nAgents
-        pp = pp + 1;
-        pairDistMat(:, pp) = vecnorm(hist.agent(jj).pos - hist.agent(kk).pos, 2, 2);
-    end
-end
-
-% Cap at communications range
-% pairDistMat = min(pairDistMat, commsRadius);
-
-% Plot all pairwise distances over time
-hold(x4, 'on');
-hLeft = gobjects(nPairs, 1);
-for pp = 1:nPairs
-    hLeft(pp) = plot(x4, pairDistMat(:, pp), 'LineWidth', 2);
-end
-yline(x4, collisionRadius, 'r--', "Label", "Collision Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
-yline(x4, commsRadius, 'r--', "Label", "Communications Radius", "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
-hold(x4, 'off');
-xlabel(x4, "Timestep");
-ylabel(x4, "Pairwise distance");
-title(x4, "$AII\beta$ Pairwise Agent Distances and Barrier Function Values", "Interpreter", "latex");
-grid(x4, "on");
-ylim(x4, [0, commsRadius + 5]);
-
-% Build legend labels
-pairLabels = strings(nPairs, 1);
-pp = 0;
-for jj = 1:nAgents-1
-    for kk = jj+1:nAgents
-        pp = pp + 1;
-        pairLabels(pp) = sprintf("Agents %d-%d Distance", jj, kk);
-    end
-end
-
-l = legend(hLeft(:), pairLabels(:), "Location", "northeast");
-
-savefig(f4, "plot4_distanceOnly.fig");
-exportgraphics(f4, "plot4_distanceOnly.png");
-
-% Plot all barrier function values on right Y-axis
-nObs = init.numObstacles;
-nAA = nchoosek(nAgents, 2);
-nAO = nAgents * nObs;
-nAD = nAgents * 6;
-nComms = size(hist.barriers, 1) - nAA - nAO - nAD;
-
-yyaxis(x4, 'right');
-hold(x4, 'on');
-
-% Color palettes: pairs share colors across collision/comms,
-% agents share colors across obstacle/domain
-pairColors = lines(nAA);
-agentColors = lines(nAgents);
-
-% Row offsets in hist.barriers
-colStart = 1;
-obsStart = colStart + nAA;
-domStart = obsStart + nAO;
-comStart = domStart + nAD;
-
-% Collision + Comms barriers grouped per pair (same color)
-hRight = gobjects(0, 1);
-rightLabels = strings(0, 1);
-for pp = 1:nAA
-    hRight(end+1) = plot(x4, hist.barriers(colStart + pp - 1, :), '--', 'LineWidth', 1.5, 'Color', pairColors(pp, :));
-    rightLabels(end+1) = sprintf('h_{col} %d', pp);
-end
-for pp = 1:nComms
-    hRight(end+1) = plot(x4, hist.barriers(comStart + pp - 1, :), '-.', 'LineWidth', 1.5, 'Color', pairColors(pp, :));
-    rightLabels(end+1) = sprintf('h_{com} %d', pp);
-end
-
-% Obstacle barriers — colored by agent
-% idx = obsStart;
-% for aa = 1:nAgents
-%     for oo = 1:nObs
-%         hRight(end+1) = plot(x4, hist.barriers(idx, :), ':', 'LineWidth', 1, 'Color', agentColors(aa, :));
-%         rightLabels(end+1) = sprintf('h_{obs} a%d-o%d', aa, oo);
-%         idx = idx + 1;
-%     end
-% end
-
-hold(x4, 'off');
-ylabel(x4, "Barrier function $h$", "Interpreter", "latex");
-
-% Clamp both Y-axes to start at 0
-yyaxis(x4, 'left');  ylim(x4, [0, 25]);
-yyaxis(x4, 'right'); ylim(x4, [0, 275]);
-x4.YAxis(2).Color = 'k';
-
-% Combined legend
-l = legend([hLeft(:); hRight(:)], [pairLabels(:); rightLabels(:)], "Location", "eastoutside");
-
-savefig(f4, "plot4.fig");
-exportgraphics(f4, "plot4.png");

plots_1_2.m

@@ -0,0 +1,174 @@
+clear;
+
+%% Load data
+dataPath = fullfile('.', 'sandbox', 'plot1');
+dataFiles = dir(dataPath);
+dataFiles = dataFiles(~startsWith({dataFiles.name}, '.'));
+simInits = dataFiles(endsWith({dataFiles.name}, 'miSimInits.mat'));
+simHists = dataFiles(endsWith({dataFiles.name}, 'miSimHist.mat'));
+assert(length(simHists) == length(simInits), "input data availability mismatch");
+
+%% Aggregate run data
+nRuns = length(simHists);
+Cfinal = NaN(nRuns, 1);
+nAgents = NaN(nRuns, 1);
+doubleIntegrator = NaN(nRuns, 1);
+numObjective = NaN(nRuns, 1);
+commsRadius = NaN(nRuns, 1);
+collisionRadius = NaN(nRuns, 1);
+maxAgents = 6;
+alphaDist = NaN(maxAgents, nRuns);
+positions = cell(maxAgents, nRuns);
+adjacency = cell(nRuns, 1);
+
+for ii = 1:nRuns
+    initName = strrep(simInits(ii).name, "_miSimInits.mat", "");
+    histName = strrep(simHists(ii).name, "_miSimHist.mat", "");
+    assert(initName == histName);
+
+    init = load(fullfile(simInits(ii).folder, simInits(ii).name));
+    hist = load(fullfile(simHists(ii).folder, simHists(ii).name));
+
+    Cfinal(ii) = hist.out.perf(end) / init.objectiveIntegral;
+    nAgents(ii) = init.numAgents;
+    doubleIntegrator(ii) = init.useDoubleIntegrator;
+    numObjective(ii) = size(init.objectivePos, 1);
+    commsRadius(ii) = unique(init.comRange);
+    collisionRadius(ii) = unique(init.collisionRadius);
+
+    adjacency{ii} = hist.out.constraintAdjacency(:, :, 1);
+    for jj = 1:nAgents(ii)
+        alphaDist(jj, ii) = hist.out.agent(jj).sensor.alphaDist;
+        positions{jj, ii} = hist.out.agent(jj).pos;
+        assert(hist.out.agent(jj).commsRadius == commsRadius(ii));
+        assert(hist.out.agent(jj).collisionRadius == collisionRadius(ii));
+    end
+end
+
+commsRadius = unique(commsRadius); assert(isscalar(commsRadius));
+collisionRadius = unique(collisionRadius); assert(isscalar(collisionRadius));
+sensorTypes = flip(unique(alphaDist(1, :)));
+nValues = sort(unique(nAgents));
+nGroups = length(nValues);
+
+%% Build config labels
+baseConfig = strings(nRuns, 1);
+for ii = 1:nRuns
+    s = "";
+    if numObjective(ii) == 1
+        s = s + "A";
+    elseif numObjective(ii) == 2
+        s = s + "B";
+    end
+    if alphaDist(1, ii) == sensorTypes(1)
+        s = s + "_I";
+    elseif alphaDist(1, ii) == sensorTypes(2)
+        s = s + "_II";
+    end
+    if ~doubleIntegrator(ii)
+        s = s + "_alpha";
+    else
+        s = s + "_beta";
+    end
+    baseConfig(ii) = s;
+end
+configOrder = unique(baseConfig(nAgents == nValues(1)), 'stable');
+nConfigs = length(configOrder);
+configLabels = ["$AI\alpha$"; "$AI\beta$"; "$AII\alpha$"; "$BI\beta$"];
+
+%% Plot 1: Final normalized coverage
+close all;
+f1 = figure;
+x1 = axes;
+
+C_mean = NaN(nGroups, nConfigs);
+C_var = NaN(nGroups, nConfigs);
+for ii = 1:nGroups
+    for jj = 1:nConfigs
+        mask = (nAgents == nValues(ii)) & (baseConfig == configOrder(jj));
+        C_mean(ii, jj) = mean(Cfinal(mask));
+        C_var(ii, jj) = var(Cfinal(mask));
+    end
+end
+
+bar(x1, C_mean);
+set(x1, 'XTickLabel', string(nValues));
+xlabel(x1, "Number of agents");
+ylabel(x1, "Final coverage (normalized)");
+title(x1, "Final performance of parameterizations");
+legend(x1, configLabels, "Interpreter", "latex", "Location", "northwest");
+grid(x1, "on");
+ylim(x1, [0, 1/2]);
+
+savefig(f1, "plot1.fig");
+exportgraphics(f1, "plot1.png");
+
+%% Plot 2: Pairwise agent distances
+f2 = figure;
+x2 = axes;
+
+% Compute pairwise distances only for connected agents (static topology)
+maxPairs = nchoosek(maxAgents, 2);
+pairDist = cell(maxPairs, nRuns);
+for ii = 1:nRuns
+    A = adjacency{ii};
+    pp = 0;
+    for jj = 1:nAgents(ii)-1
+        for kk = jj+1:nAgents(ii)
+            pp = pp + 1;
+            if A(jj, kk)
+                pairDist{pp, ii} = vecnorm(positions{jj, ii} - positions{kk, ii}, 2, 2);
+            end
+        end
+    end
+end
+
+% Per-run statistics across all pairs and timesteps
+meanPairDist = NaN(nRuns, 1);
+minPairDist = NaN(nRuns, 1);
+maxPairDist = NaN(nRuns, 1);
+for ii = 1:nRuns
+    nPairs = nchoosek(nAgents(ii), 2);
+    D = vertcat(pairDist{1:nPairs, ii});
+    meanPairDist(ii) = mean(D, "omitmissing");
+    minPairDist(ii) = min(D);
+    maxPairDist(ii) = max(D);
+end
+
+% Aggregate across trials per (n, config) group
+meanD = NaN(nGroups, nConfigs);
+minD = NaN(nGroups, nConfigs);
+maxD = NaN(nGroups, nConfigs);
+for ii = 1:nGroups
+    for jj = 1:nConfigs
+        mask = (nAgents == nValues(ii)) & (baseConfig == configOrder(jj));
+        meanD(ii, jj) = mean(meanPairDist(mask));
+        minD(ii, jj) = min(minPairDist(mask));
+        maxD(ii, jj) = max(maxPairDist(mask));
+    end
+end
+
+% Plot whiskers (min to max) with mean markers
+barWidth = 0.8;
+groupWidth = barWidth / nConfigs;
+hold(x2, 'on');
+for jj = 1:nConfigs
+    xPos = (1:nGroups) + (jj - (nConfigs + 1) / 2) * groupWidth;
+    errorbar(x2, xPos, meanD(:, jj), meanD(:, jj) - minD(:, jj), maxD(:, jj) - meanD(:, jj), ...
+        'o', 'LineWidth', 1.5, 'MarkerSize', 6, 'CapSize', 10);
+end
+hold(x2, 'off');
+set(x2, 'XTick', 1:nGroups, 'XTickLabel', string(nValues));
+xlabel(x2, "Number of agents");
+ylabel(x2, "Pairwise distance");
+title(x2, "Pairwise Agent Distances (min/mean/max)");
+legend(x2, configLabels, "Interpreter", "latex");
+grid(x2, "on");
+yline(x2, collisionRadius, 'r--', "Label", "Collision Radius", ...
+    "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+yline(x2, commsRadius, 'r--', "Label", "Communications Radius", ...
+    "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+ylim(x2, [0, commsRadius + 5]);
+
+savefig(f2, "plot2.fig");
+exportgraphics(f2, "plot2.png");

plots_3_4.m

@@ -0,0 +1,120 @@
+clear;
+
+%% Load data
+dataPath = fullfile('.', 'sandbox', 'plot3');
+dataFiles = dir(dataPath);
+dataFiles = dataFiles(~startsWith({dataFiles.name}, '.'));
+simInits = dataFiles(endsWith({dataFiles.name}, 'miSimInits.mat'));
+simHists = dataFiles(endsWith({dataFiles.name}, 'miSimHist.mat'));
+assert(length(simHists) == length(simInits), "input data availability mismatch");
+assert(isscalar(simHists));
+
+init = load(fullfile(simInits(1).folder, simInits(1).name));
+hist = load(fullfile(simHists(1).folder, simHists(1).name));
+hist = hist.out;
+
+%% Plot 3: Per-agent and cumulative normalized performance
+assert(size(init.objectivePos, 1) == 1);
+assert(hist.useDoubleIntegrator);
+
+nAgents = length(hist.agent);
+agentLabels = "Agent " + string(1:nAgents)';
+
+f3 = figure;
+x3 = axes;
+hold(x3, 'on');
+plot(x3, hist.perf ./ init.objectiveIntegral, "LineWidth", 2);
+for ii = 1:nAgents
+    plot(x3, hist.agent(ii).perf ./ init.objectiveIntegral, "LineWidth", 2);
+end
+hold(x3, 'off');
+grid(x3, "on");
+ylabel(x3, "Performance (normalized)");
+xlabel(x3, "Timestep");
+legend(x3, ["Cumulative"; agentLabels], "Location", "northwest");
+title(x3, "$AII\beta$ Performance", "Interpreter", "latex");
+
+savefig(f3, "plot3.fig");
+exportgraphics(f3, "plot3.png");
+
+%% Plot 4: Pairwise distances and barrier functions
+commsRadius = hist.agent(1).commsRadius;
+collisionRadius = hist.agent(1).collisionRadius;
+nPairs = nchoosek(nAgents, 2);
+T = size(hist.agent(1).pos, 1);
+
+% Compute pairwise distances over time
+pairDistMat = NaN(T, nPairs);
+pairLabels = strings(nPairs, 1);
+pp = 0;
+for jj = 1:nAgents-1
+    for kk = jj+1:nAgents
+        pp = pp + 1;
+        pairDistMat(:, pp) = vecnorm(hist.agent(jj).pos - hist.agent(kk).pos, 2, 2);
+        pairLabels(pp) = sprintf("Agents %d-%d Distance", jj, kk);
+    end
+end
+
+f4 = figure;
+x4 = axes;
+
+% Left Y-axis: pairwise distances
+hold(x4, 'on');
+hLeft = gobjects(nPairs, 1);
+for pp = 1:nPairs
+    hLeft(pp) = plot(x4, pairDistMat(:, pp), 'LineWidth', 2);
+end
+yline(x4, collisionRadius, 'r--', "Label", "Collision Radius", ...
+    "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+yline(x4, commsRadius, 'r--', "Label", "Communications Radius", ...
+    "LabelHorizontalAlignment", "left", "HandleVisibility", "off");
+hold(x4, 'off');
+xlabel(x4, "Timestep");
+ylabel(x4, "Pairwise distance");
+title(x4, "$AII\beta$ Pairwise Agent Distances and Barrier Function Values", "Interpreter", "latex");
+grid(x4, "on");
+
+savefig(f4, "plot4_distanceOnly.fig");
+exportgraphics(f4, "plot4_distanceOnly.png");
+
+% Right Y-axis: barrier function values
+nObs = init.numObstacles;
+nAA = nchoosek(nAgents, 2);
+nAO = nAgents * nObs;
+nAD = nAgents * 6;
+nComms = size(hist.barriers, 1) - nAA - nAO - nAD;
+
+colStart = 1;
+comStart = colStart + nAA + nAO + nAD;
+
+pairColors = lines(nAA);
+
+yyaxis(x4, 'right');
+hold(x4, 'on');
+hRight = gobjects(nAA + nComms, 1);
+rightLabels = strings(nAA + nComms, 1);
+idx = 0;
+for pp = 1:nAA
+    idx = idx + 1;
+    hRight(idx) = plot(x4, hist.barriers(colStart + pp - 1, :), '--', ...
+        'LineWidth', 1.5, 'Color', pairColors(pp, :));
+    rightLabels(idx) = sprintf('h_{col} %d', pp);
+end
+for pp = 1:nComms
+    idx = idx + 1;
+    hRight(idx) = plot(x4, hist.barriers(comStart + pp - 1, :), '-.', ...
+        'LineWidth', 1.5, 'Color', pairColors(pp, :));
+    rightLabels(idx) = sprintf('h_{com} %d', pp);
+end
+hold(x4, 'off');
+ylabel(x4, "Barrier function $h$", "Interpreter", "latex");
+
+% Y-axis limits
+yyaxis(x4, 'left');  ylim(x4, [0, 25]);
+yyaxis(x4, 'right'); ylim(x4, [0, 275]);
+x4.YAxis(2).Color = 'k';
+
+legend([hLeft(:); hRight(:)], [pairLabels(:); rightLabels(:)], "Location", "eastoutside");
+
+savefig(f4, "plot4.fig");
+exportgraphics(f4, "plot4.png");

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/Dt-U7DFYhio-3hILJY9YWPnda6sp.xml

@@ -1,2 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Info location="plot_1.m" type="File"/>

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/FAIFc6EGkYrae07xGB2jWoFdM-0p.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info location="plots_3_4.m" type="File"/>

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/cQhJ0S-hbiRJRVZSMGQe9M3c01Yp.xml

@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<Info location="plots_1_2.m" type="File"/>

resources/project/qaw0eS1zuuY1ar9TdPn1GMfrjbQ/e-EiR3xDuOjis31Xf2_ZV1uoriEp.xml

@@ -1,2 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Info location="plot_3.m" type="File"/>

test/results.m

@@ -10,8 +10,8 @@ classdef results < matlab.unittest.TestCase
 
         %% Diagnostic Parameters
         % No effect on simulation dynamics
-        makeVideo = true; % disable video writing for big performance increase
-        makePlots = true; % disable plotting for big performance increase (also disables video)
+        makeVideo = false; % disable video writing for big performance increase
+        makePlots = false; % disable plotting for big performance increase (also disables video)
         plotCommsGeometry = false; % disable plotting communications geometries
 
         %% Scenario Reinitialization
@@ -72,8 +72,8 @@ classdef results < matlab.unittest.TestCase
             sensor2 = sigmoidSensor;
             sensor1 = sensor1.initialize(sensors(1).alphaDist, sensors(1).betaDist, sensors(1).alphaTilt, sensors(1).betaTilt);
             sensor2 = sensor2.initialize(sensors(2).alphaDist, sensors(2).betaDist, sensors(2).alphaTilt, sensors(2).betaTilt);
-            sensor1.plotParameters;
-            sensor2.plotParameters;
+            % sensor1.plotParameters;
+            % sensor2.plotParameters;
             c = struct('A_1_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', false), ...
                         'A_1_beta',  struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(1), 'doubleIntegrator', true), ...
                         'A_2_alpha', struct('objectivePos', [3, 1] / 4 .* results.domainSize(1:2), 'sensor', sensors(2), 'doubleIntegrator', false), ...
@@ -271,8 +271,8 @@ classdef results < matlab.unittest.TestCase
         end
         function AIIbeta_plots_3_4(tc)
             % test-specific parameters
-            tc.makePlots = true;
-            tc.makeVideo = true;
+            tc.makePlots = false;
+            tc.makeVideo = false;
             maxIters = 400;
 
             configs = results.makeConfigs();